Differentiating cytology of pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors by EUS-FNA through hyperspectral imaging technology combined with artificial intelligence.

ABSTRACT:

Background

Pancreatic cancer is a common and lethal malignancy, with the two primary subtypes being pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine tumors (pNET). Accurate diagnosis and effective treatment are crucial. Hyperspectral imaging (HSI) is a novel optical diagnostic technology that can capture spectral features inaccessible by traditional imaging techniques. With the aid of artificial intelligence (AI), HSI can provide richer information.

Objectives

This study aims to develop a convolutional neural network (CNN) based on HSI to assist in the diagnosis of liquid-based cytology (LBC) specimens of PDAC and pNET obtained by endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA).

Design

We designed a deep learning model using HSI data

SUBMITTER: Qin X 

PROVIDER: S-EPMC12799998 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature